Co-incineration of sewage sludge in coal fired power plants.Because of the growth of population and industrial applications more and more waste water will be produced. That leads to a increasing appearance of sewage sludge in the future. At the same time the available space on dump sites for land filling the sewage sludge will decrease. The usage of sewage sludge in agriculture as fertilizer is controversial because besides over-fertilisation heavy metal accumulation in soil will take place. The most prosperous way of dealing with dewatered sewage sludge in the future is the co-incineration for exaple in coal fired power plants. Putzmeister as a system supplier has developed a solution for co-incineration of mechanically dewatered sewage sludge in coal fired power plants. System advantages: -Technically secured handling method, approved power plant technique with flue gas cleaning can be used. -Organic compounds in the sewage sludge like, dioxin and furan will be completely destroyed by being incinerated at more than 1000°C -Also the mineral fraction of the sewage sludge can be utilized together with the coal ash in the construction industry. -Cost economic option compared with co-incineration of sewage sludge in household waste incineration plants and with pure sewage sludge incineration plants.
In the practice of company management we deal with many tasks, that are associated with limited knowledge and uncertainty about the course of events and activities managed objects. Fuzzy IF-THEN rules are an appropriate form of describing subjective uncertainty results from lack of knowledge and objective uncertainty results from characteristics of different processes. On the other hand, the uncertainty due to randomness can be described using the theory of probability. The paper presents inference system with probabilistic-fuzzy knowledge base as a tool which can help user to analyze complete uncertainty of real problems in the company using fuzzy sets and probability. In the mentioned system, knowledge is saved in the weighted IF-THEN fuzzy rules, where the weights constitute marginal probabilities of the fuzzy events in the antecedents and conditional probabilities of the fuzzy events in the consequents. Moreover, this paper propose using fuzzy association rules as a method of automatic knowledge base extraction in the inference system. For this purpose a modification of the Apriori algorithm was described. The algorithm extracts the most important and matching linguistic rules by assumption of minimum support as a minimum joint probability of the events in the rules. If minimum support equals zero, then the rules present total probabilistic distribution of the fuzzy events, otherwise the rules present probabilistic distribution, which is the best matching to a variables universe. In the methodology of system creation, the universe of quantitative variables is discretized on disjoint intervals of variable values and the fuzzy sets are defined by grades of membership of the disjoint intervals to fuzzy sets. This approach allows vectorize the calculation. A numerical example is analyzed by using a wind speed prediction process. Parameter of wind speed characterized by high variability of random character. However, the correct estimation of wind speed, as a energy resources, is necessary for control working of wind turbine. It is also important for the localization process of wind turbines, production planning and estimating cost-effectiveness of such investments.
Logistics service providers offer a whole or partial logistics business service over a certain time period. Between such companies, the effectiveness of specific logistics services can vary. Logistics service providers seek the effective performance of logistics service. The purpose of this paper is to present a new approach for the evaluation of logistics service effectiveness, along with a specific computer system implementing the proposed approach - a sophisticated inference system, an extension of the Mamdani probabilistic fuzzy system. The paper presents specific knowledge concerning the relationships between effectiveness indicators in the form of fuzzy rules which contain marginal and conditional probabilities of fuzzy events. An inference diagram is also shown. A family of Yager’s parameterized t-norms is proposed as inference operators. It facilitates the optimization of system parameters and enables flexible adjustment of the system to empirical data. A case study was used to illustrate the new approach for the evaluation of logistics service effectiveness. The approach is demonstrated on logistics services in a logistics company. We deem the analysis of a probabilistic fuzzy knowledge base to be useful for the evaluation of effectiveness of logistics services in a logistics company over a given time period.